class_docstr: ProcessGroup
  Process Group indicates how processes are organized in groups for parallel execution using Tensor Parallelism and Data Parallelism.
  
  NOTE, the ProcessGroup must be used after `torch.distributed.initialize()`
  
  
  Args:
      rank: the global rank of the current process.
      ranks: List[int], a list of rank id belongings to this process group.
      backend: str, the backend of the process group.
      tp_degree: Optional[int], tensor parallelism degree. How many processes are inside a tp process group. default None means 1.
      dp_degree: Optional[int], data parallelism degree. How many processes are inside a dp process group. . default None means len(ranks).
docstr: ranks_in_group
  
  a list of rank number in in the global process group.
  
  Returns:
      List[int]: a list of rank number.
docstr_tok: ["ranks_in_group","a","list","of","rank","number","in","in","the","global","process","group",".","Returns",":","List","[","int","]",":","a","list","of","rank","number","."]
code: def ranks_in_group(self) -> List[int]:
      """ranks_in_group
  
      a list of rank number in in the global process group.
  
      Returns:
          List[int]: a list of rank number.
      """
      return self._rank_list
  
imports: from typing import List, Optional
  import torch
  from colossalai.context.singleton_meta import SingletonMeta
  from colossalai.logging import get_dist_logger
label_desc: Use image node_id 7 for calling the ProcessGroup obj's underlying member method code with example usage: obj.ranks_in_group() and returns: self